Researchers in Spain have identified three specific proteins in human saliva that could enable the early detection of Alzheimer’s disease long before clinical symptoms manifest, according to reports.
This development offers a potential new avenue for identifying the condition in its nascent stages.
Separately, advancements in neuroimaging are providing further tools for early intervention. A study published in the journal Science Advances and coordinated by researchers at Stanford Medicine has identified specific patterns of brain activity that may predict the risk of Alzheimer’s-related cognitive decline up to 12 years before clinical symptoms appear, as noted by HotNews.
To analyze these neurological indicators, researchers developed a system known as ARC (Alzheimer’s-resilient connectome). This assessment tool is designed to distinguish between brain patterns associated with vulnerability to cognitive decline and those linked to the preservation of cognitive capacities in older age. The system was tested on an independent group of 143 participants, following initial success in differentiating individuals with high cognitive performance from patients already diagnosed with Alzheimer’s.
The research team applied the ARC method to two additional independent groups consisting of 818 and 497 individuals, respectively. These participants were aged between 55 and 70 and showed no cognitive impairments at the time of examination. By comparing the ARC system’s predictive capacity against established indicators—such as beta-amyloid and tau biomarkers, genetic characteristics, and demographic data—scientists aim to move beyond traditional testing.
Current research often relies on biomarkers that are typically observable only in relatively advanced stages of the disease. The goal of this new approach is to define indicators that detect the earliest signs of decline, potentially allowing for timely intervention. The findings suggest that analyzing brain connectivity could eventually serve as a vital complement to current methods for identifying individuals at high risk for Alzheimer’s.
